In [ ]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
In [ ]:
# Load data
P8_C_F_otus_data = pd.read_csv('./P8_C_F/data_normalized.csv',sep=',',index_col=0)
P8_C_F_metadata = pd.read_csv('./P8_C_F/P8_C_F.csv',sep=',',index_col=0)
P8_C_F_otus_data.head(10)
Out[ ]:
1 2 3 4 5 6 7 8 9 10 ... 40 41 42 43 44 45 46 47 48 49
Lactobacillus 8.897736e+06 8.567618e+06 8.742705e+06 2.759672e+05 6.973386e+05 2.561120e+05 8.943864e+06 8.768577e+06 1.449831e+06 5.038006e+05 ... 5.294519e+06 7.169531e+06 6.686188e+06 5.306953e+06 3.559897e+06 7.753555e+05 5.392391e+06 5.250797e+06 8.060608e+06 7.510279e+06
Escherichia_Shigella 1.444014e+04 1.423959e+04 7.019514e+03 8.793847e+06 7.525722e+06 7.659895e+06 1.421953e+05 6.618399e+03 7.227492e+06 7.914201e+06 ... 4.294138e+06 1.897475e+06 2.988709e+06 4.332845e+06 5.436514e+06 8.336977e+06 4.162773e+06 4.361726e+06 5.597561e+05 1.748060e+06
Muribacter 1.642566e+05 1.349752e+05 1.772929e+05 8.022302e+03 2.843906e+05 8.343194e+04 1.464070e+05 2.761677e+05 4.432322e+04 2.202122e+05 ... 3.850705e+04 8.423417e+04 4.773270e+04 3.409478e+04 8.824532e+03 1.925352e+04 6.959347e+04 7.440685e+04 3.210926e+05 1.564349e+05
Staphylococcus 1.772929e+05 4.336054e+05 3.461623e+05 1.427970e+05 8.575841e+05 7.300295e+04 8.022302e+02 1.002788e+03 7.364473e+05 1.007200e+06 ... 2.607248e+03 3.610036e+03 4.011151e+03 2.807806e+03 2.787750e+04 9.827320e+03 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
Streptococcus 5.653717e+05 5.900403e+05 5.286697e+05 5.415054e+03 6.137061e+04 2.386635e+04 5.633662e+05 7.573053e+05 2.547081e+04 9.907543e+04 ... 2.264295e+05 2.859951e+05 1.768918e+05 2.097832e+05 1.929364e+05 7.681354e+04 2.360562e+05 2.200116e+05 8.792443e+05 4.207697e+05
Enterococcus 1.062955e+04 2.807806e+03 1.403903e+03 1.283568e+04 8.423417e+03 3.228977e+04 2.607248e+03 3.610036e+03 8.824532e+03 5.615611e+03 ... 2.607248e+03 4.011151e+03 5.415054e+03 1.584405e+04 1.042899e+04 4.612824e+03 1.604460e+03 2.206133e+03 1.403903e+03 3.208921e+03
Pseudomonas 0.000000e+00 2.005575e+02 2.005575e+02 0.000000e+00 0.000000e+00 3.208921e+03 0.000000e+00 0.000000e+00 0.000000e+00 1.604460e+03 ... 0.000000e+00 0.000000e+00 0.000000e+00 2.005575e+02 4.011151e+02 1.203345e+03 0.000000e+00 8.022302e+02 2.005575e+02 0.000000e+00
Lachnospiraceae_NK4A136_group 4.612824e+03 1.343736e+04 1.403903e+04 3.971039e+04 3.088586e+04 1.079000e+05 8.824532e+03 1.183290e+04 2.767694e+04 1.383847e+04 ... 7.621187e+03 2.908084e+04 5.013939e+03 5.615611e+03 4.652935e+04 4.672991e+04 9.025090e+03 1.604460e+03 1.243457e+04 8.824532e+03
Muribaculaceae 3.228977e+04 5.856280e+04 3.329255e+04 1.961453e+05 1.379836e+05 4.362127e+05 4.793325e+04 4.211709e+04 1.472092e+05 7.139849e+04 ... 3.770482e+04 1.367802e+05 2.065743e+04 2.266300e+04 1.937386e+05 1.867191e+05 2.527025e+04 2.105854e+04 5.134273e+04 4.652935e+04
Lachnospiraceae 1.805018e+03 4.612824e+03 4.011151e+03 2.065743e+04 9.225647e+03 4.973827e+04 5.013939e+03 3.610036e+03 7.019514e+03 4.612824e+03 ... 2.206133e+03 1.083011e+04 1.805018e+03 2.005575e+03 1.805018e+04 2.446802e+04 2.206133e+03 1.805018e+03 3.008363e+03 1.805018e+03

10 rows × 48 columns

In [ ]:
def sort_meta(P8_C_F_metadata,condition = "Experiment"):
    new_meta_index = []
    for index1 in P8_C_F_metadata.index:
        new_meta_index.append(str(index1))
    P8_C_F_metadata.index = new_meta_index
    metadata = P8_C_F_metadata.sort_values(condition)
    return metadata
metadata = sort_meta(P8_C_F_metadata)
metadata.head(10)
Out[ ]:
Experiment
1 C
47 C
46 C
43 C
42 C
41 C
40 C
30 C
48 C
24 C
In [ ]:
def create_heatmap(otus_data,metadata,condition ="Experiment" ):
    heatmap = otus_data
    otus_data = otus_data.transpose()
    new_column = []
    new_idx = []
    for index1 in otus_data.index:
        new_idx.append(str(index1))
    otus_data.index = new_idx
    # print(otus_data)
    for index1 in otus_data.index:
        for index2 in metadata.index:
            value = metadata.loc[index2, condition]
            if str(index1) == str(index2):
                new_column.append(value)
    otus_data[condition] = new_column
    otus_data = otus_data.sort_values(by=condition)
    # print(otus_data)
    heatmap = otus_data.drop(columns=[condition])
    heatmap = heatmap.transpose()
    return heatmap
heatmap = create_heatmap(P8_C_F_otus_data,P8_C_F_metadata)
heatmap.head(10)
Out[ ]:
1 47 46 43 42 41 40 30 48 24 ... 26 9 10 21 20 19 18 17 34 32
Lactobacillus 8.897736e+06 5.250797e+06 5.392391e+06 5.306953e+06 6.686188e+06 7.169531e+06 5.294519e+06 6.901988e+06 8.060608e+06 6.695213e+06 ... 9.580634e+05 1.449831e+06 5.038006e+05 1.655402e+06 3.256052e+06 5.003911e+05 1.514410e+06 1.949419e+05 7.741521e+04 2.219169e+06
Escherichia_Shigella 1.444014e+04 4.361726e+06 4.162773e+06 4.332845e+06 2.988709e+06 1.897475e+06 4.294138e+06 2.847316e+06 5.597561e+05 2.905678e+06 ... 7.556006e+06 7.227492e+06 7.914201e+06 7.102345e+06 5.918052e+06 8.609133e+06 8.115962e+06 8.472754e+06 8.809490e+06 7.340206e+06
Muribacter 1.642566e+05 7.440685e+04 6.959347e+04 3.409478e+04 4.773270e+04 8.423417e+04 3.850705e+04 7.881912e+04 3.210926e+05 2.005575e+04 ... 4.011151e+02 4.432322e+04 2.202122e+05 3.870761e+04 7.821744e+03 3.389423e+04 1.484126e+04 1.103067e+04 2.647360e+04 1.444014e+04
Staphylococcus 1.772929e+05 0.000000e+00 0.000000e+00 2.807806e+03 4.011151e+03 3.610036e+03 2.607248e+03 0.000000e+00 0.000000e+00 7.019514e+03 ... 1.002788e+04 7.364473e+05 1.007200e+06 3.329255e+04 9.506428e+04 1.873208e+05 1.209362e+05 6.297507e+04 0.000000e+00 1.805018e+03
Streptococcus 5.653717e+05 2.200116e+05 2.360562e+05 2.097832e+05 1.768918e+05 2.859951e+05 2.264295e+05 1.399892e+05 8.792443e+05 2.454824e+05 ... 2.406691e+03 2.547081e+04 9.907543e+04 4.412266e+03 1.644572e+04 2.426746e+04 2.888029e+04 2.908084e+04 8.022302e+02 9.225647e+03
Enterococcus 1.062955e+04 2.206133e+03 1.604460e+03 1.584405e+04 5.415054e+03 4.011151e+03 2.607248e+03 0.000000e+00 1.403903e+03 1.002788e+03 ... 1.484126e+04 8.824532e+03 5.615611e+03 1.022844e+04 4.612824e+03 7.220072e+03 3.409478e+03 1.524237e+04 1.022844e+04 2.807806e+03
Pseudomonas 0.000000e+00 8.022302e+02 0.000000e+00 2.005575e+02 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.005575e+02 0.000000e+00 ... 3.610036e+03 0.000000e+00 1.604460e+03 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.203345e+03 0.000000e+00 4.011151e+02
Lachnospiraceae_NK4A136_group 4.612824e+03 1.604460e+03 9.025090e+03 5.615611e+03 5.013939e+03 2.908084e+04 7.621187e+03 3.008363e+03 1.243457e+04 6.417842e+03 ... 6.818957e+04 2.767694e+04 1.383847e+04 7.139849e+04 4.332043e+04 3.108642e+04 9.426205e+03 6.137061e+04 5.796113e+04 2.346523e+04
Muribaculaceae 3.228977e+04 2.105854e+04 2.527025e+04 2.266300e+04 2.065743e+04 1.367802e+05 3.770482e+04 3.409478e+03 5.134273e+04 3.008363e+04 ... 3.006358e+05 1.472092e+05 7.139849e+04 2.525020e+05 1.652594e+05 1.413931e+05 4.993883e+04 2.900062e+05 3.036441e+05 9.165480e+04
Lachnospiraceae 1.805018e+03 1.805018e+03 2.206133e+03 2.005575e+03 1.805018e+03 1.083011e+04 2.206133e+03 0.000000e+00 3.008363e+03 6.016726e+02 ... 3.489701e+04 7.019514e+03 4.612824e+03 4.131486e+04 1.805018e+04 2.125910e+04 2.206133e+03 3.349311e+04 3.028419e+04 7.621187e+03

10 rows × 48 columns

In [ ]:
def colordict(metadata,condition ='Experiment' ):
    color_dict=dict(zip(np.unique(metadata[condition]),np.array(['g','blue'])))
    row_colors = metadata[condition].map(color_dict)
    return color_dict,row_colors
color_dict,row_colors = colordict(metadata)
In [ ]:
row_colors.head(10)
Out[ ]:
1     g
47    g
46    g
43    g
42    g
41    g
40    g
30    g
48    g
24    g
Name: Experiment, dtype: object
In [ ]:
color_dict
Out[ ]:
{'C': 'g', 'F': 'blue'}

P8 Control vs Feeding (Genus)¶

In [ ]:
def plot_cluster_heatmap(heatmap,color_dict,row_colors,title):
    custom_cmap = sns.color_palette("OrRd", as_cmap=True)
    hm = sns.clustermap(heatmap,
                metric="correlation",
                standard_scale=0,
                z_score=None,
                col_colors=row_colors,
                col_cluster=False,
                cmap=custom_cmap,
                # cbar_pos=(0, .2, .03, .4),
                figsize=(14, 12))
    # Create a color legend using the color_dict
    legend_labels = [f"{experiment}" for experiment, color in color_dict.items()]
    legend_colors = [color for _, color in color_dict.items()]
    legend_handles = [plt.Line2D([0], [0], marker='o', color='w', label=label, markersize=10, markerfacecolor=color) for label, color in zip(legend_labels, legend_colors)]
    plt.legend(handles=legend_handles, title="Experiment", bbox_to_anchor=(15, 1), loc='upper left')
    # Add a title to the center of the heatmap
    ax  = hm.ax_heatmap
    ax.text(0.5, 1.1, title, fontsize=12, ha="center", va="center", transform=ax.transAxes)
    # Get the current Axes objects
    ax_row_labels = hm.ax_row_dendrogram
    ax_col_labels = hm.ax_col_dendrogram

    # Set row and column labels font size
    row_font_size = 4
    col_font_size = 4

    for label in ax_row_labels.get_yticklabels():
        label.set_fontsize(row_font_size)

    for label in ax_col_labels.get_xticklabels():
        label.set_fontsize(col_font_size)

    # Display the plot
    plt.show()
title = "P8 Control vs Feeding (Genus)"
plot_cluster_heatmap(heatmap,color_dict,row_colors,title)
No description has been provided for this image

P11_NEC_Non-NEC_Colon¶

Load datasets¶

In [ ]:
# Load data
P11_C_NEC_otus_data = pd.read_csv('./P11_NEC_Non-NEC/Colon/tsv/data_normalized.csv',sep=',',index_col=0)
P11_C_NEC_metadata = pd.read_csv('./P11_NEC_Non-NEC/Colon/tsv/NEC_Colon.csv',sep=',',index_col=0)
P11_C_NEC_otus_data.head(10)
Out[ ]:
050A 051A 052A 053A 054A 055A 056A 057A 058A 059A ... 239A 240A 241A 242A 243A 244A 245A 246A 247A 248A
Lactobacillus 6.057526e+06 6.305880e+06 7.359362e+06 2.354164e+06 2.067691e+06 1.836664e+06 2.043433e+06 8.016634e+05 5.478803e+06 5.900427e+06 ... 5.451080e+06 4.942821e+06 9.922606e+05 9.691579e+05 4.285549e+05 1.417350e+06 1.172462e+06 1.069655e+06 1.736167e+06 1.845905e+06
Escherichia_Shigella 3.487351e+06 3.416888e+06 2.185515e+06 5.077972e+06 4.606677e+06 6.169574e+06 5.613954e+06 6.446806e+06 4.056833e+06 3.863925e+06 ... 2.090794e+05 2.136999e+05 1.773132e+06 4.032575e+06 1.888645e+06 5.925840e+05 1.147049e+06 1.929075e+06 2.555158e+06 2.952524e+06
Muribacter 2.079242e+04 3.003350e+04 2.194756e+04 2.310269e+03 4.158484e+04 1.963729e+04 1.108929e+05 0.000000e+00 4.042971e+04 1.039621e+04 ... 2.495091e+05 1.686496e+05 4.505025e+04 0.000000e+00 0.000000e+00 8.085942e+03 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
Staphylococcus 0.000000e+00 3.465404e+03 0.000000e+00 1.686496e+06 1.957953e+06 8.085942e+05 1.058103e+06 1.145893e+06 4.620538e+03 2.310269e+03 ... 4.505025e+04 4.273998e+04 5.105695e+05 3.361442e+05 2.021486e+05 5.198106e+04 2.194756e+05 4.782257e+05 1.825113e+05 4.505025e+05
Streptococcus 2.691464e+05 1.016518e+05 2.668361e+05 5.313619e+04 1.420816e+05 2.310269e+05 8.201455e+04 2.425783e+04 3.130415e+05 1.155135e+05 ... 5.105695e+05 6.283932e+05 1.547880e+05 3.696431e+04 1.848215e+04 3.234377e+04 2.079242e+04 2.772323e+04 4.620538e+03 1.732702e+04
Klebsiella 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
Enterococcus 4.389511e+04 1.501675e+04 4.620538e+03 8.259212e+05 1.180548e+06 9.345039e+05 3.742636e+05 1.572138e+06 1.732702e+04 0.000000e+00 ... 2.841631e+05 7.947326e+05 1.330715e+06 1.505140e+06 1.421971e+06 1.443918e+05 8.744369e+05 3.696431e+05 9.321936e+05 3.892804e+05
Pseudomonas 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 0.000000e+00 0.000000e+00 0.000000e+00 3.465404e+03 0.000000e+00 0.000000e+00 0.000000e+00 3.465404e+03 4.620538e+03 0.000000e+00
Lachnospiraceae_NK4A136_group 1.212891e+05 1.201340e+05 1.432367e+05 0.000000e+00 1.155135e+03 0.000000e+00 6.642024e+05 5.775673e+03 4.042971e+04 3.465404e+04 ... 5.175003e+05 7.508375e+04 2.824304e+06 1.674945e+05 2.223634e+06 1.187478e+06 7.242694e+05 5.429132e+05 2.790805e+06 1.498210e+06
Muribaculaceae 0.000000e+00 0.000000e+00 2.310269e+03 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 8.940742e+05 5.232760e+05 7.970429e+04 7.554580e+05 3.465404e+03 2.238651e+06 2.984868e+06 4.469216e+06 3.465404e+03 0.000000e+00

10 rows × 90 columns

Sort metadata file¶

In [ ]:
P11_C_NEC_metadata = sort_meta(P11_C_NEC_metadata)
P11_C_NEC_metadata.head(10)
Out[ ]:
Experiment
050A C
085A C
086A C
087A C
088A C
095A C
097A C
102A C
103A C
104A C

Build color dictionary¶

In [ ]:
P11_C_NEC_color_dict,P11_C_NEC_row_colors = colordict(P11_C_NEC_metadata)
P11_C_NEC_color_dict
Out[ ]:
{'C': 'g', 'NEC': 'blue'}

Build Heatmap Matrix¶

In [ ]:
P11_C_NEC_otus_data_heatmap = create_heatmap(P11_C_NEC_otus_data,P11_C_NEC_metadata)
P11_C_NEC_otus_data_heatmap.head(10)
Out[ ]:
050A 086A 087A 088A 095A 096A 102A 103A 104A 105A ... 071A 070A 069A 068A 217A 218A 219A 221A 223A 248A
Lactobacillus 6.057526e+06 5.326326e+06 7.571907e+06 6.762158e+06 5.340187e+06 6.402911e+06 8.668130e+06 8.635786e+06 5.210812e+06 8.074391e+06 ... 6.512649e+06 8.744369e+05 4.429941e+06 6.722883e+06 2.529745e+05 2.079242e+05 1.386161e+04 3.868546e+06 2.262909e+06 1.845905e+06
Escherichia_Shigella 3.487351e+06 4.218551e+06 9.125563e+05 1.373455e+06 2.945593e+06 4.089176e+05 2.772323e+05 3.927458e+04 3.095761e+05 5.509992e+05 ... 2.194756e+04 1.386161e+04 1.386161e+04 5.775673e+03 5.186554e+05 2.161257e+06 6.091025e+06 1.333025e+06 1.692272e+06 2.952524e+06
Muribacter 2.079242e+04 2.772323e+04 9.010050e+04 1.674945e+05 4.620538e+03 3.003350e+04 4.019868e+05 5.879635e+05 2.503177e+06 5.255862e+05 ... 2.310269e+03 1.501675e+04 1.536329e+05 1.963729e+04 6.930807e+03 1.316853e+05 1.963729e+04 2.252512e+05 0.000000e+00 0.000000e+00
Staphylococcus 0.000000e+00 5.775673e+03 2.310269e+04 6.237727e+04 6.930807e+03 1.386161e+04 0.000000e+00 0.000000e+00 1.848215e+04 0.000000e+00 ... 1.157445e+06 1.617188e+04 2.633707e+05 1.371145e+06 8.085942e+04 2.760772e+05 8.779023e+04 3.419198e+05 3.118863e+05 4.505025e+05
Streptococcus 2.691464e+05 2.841631e+05 8.386277e+05 7.670094e+05 1.328405e+05 2.922490e+05 4.562782e+05 6.526510e+05 7.288899e+05 4.886219e+05 ... 9.703130e+04 9.472104e+04 4.158484e+04 2.136999e+05 1.039621e+04 1.386161e+04 2.310269e+03 4.031420e+05 1.386161e+04 1.732702e+04
Klebsiella 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 4.135382e+05 2.172808e+06 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 5.660159e+04 2.541296e+04 8.085942e+03 0.000000e+00 0.000000e+00 0.000000e+00
Enterococcus 4.389511e+04 4.851565e+04 1.201340e+05 2.541296e+04 3.003350e+04 3.003350e+04 2.656810e+04 4.389511e+04 3.141966e+05 4.273998e+04 ... 2.171653e+06 4.145778e+06 5.058334e+06 1.644912e+06 2.656810e+04 7.161834e+04 2.663740e+06 3.349890e+05 1.363059e+06 3.892804e+05
Pseudomonas 0.000000e+00 0.000000e+00 1.155135e+04 9.241077e+03 2.310269e+03 6.930807e+03 1.039621e+04 2.310269e+04 7.808710e+05 1.155135e+03 ... 3.465404e+04 9.703130e+04 3.696431e+04 2.194756e+04 2.310269e+03 1.270648e+04 8.085942e+03 0.000000e+00 0.000000e+00 0.000000e+00
Lachnospiraceae_NK4A136_group 1.212891e+05 1.848215e+04 2.772323e+04 6.122213e+04 4.158484e+04 4.273998e+04 1.259097e+05 8.085942e+03 2.656810e+04 9.125563e+04 ... 0.000000e+00 1.266027e+06 0.000000e+00 0.000000e+00 2.772323e+05 4.417235e+06 1.501675e+04 4.920873e+05 3.014901e+05 1.498210e+06
Muribaculaceae 0.000000e+00 1.039621e+04 5.660159e+04 1.247545e+05 6.907705e+05 3.003350e+04 5.775673e+03 0.000000e+00 0.000000e+00 2.772323e+04 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 3.539332e+06 5.775673e+03 0.000000e+00 8.894536e+05 0.000000e+00 0.000000e+00

10 rows × 90 columns

In [ ]:
title = "P11 Control vs NEC Colon (Genus)"
# plot_cluster_heatmap(P11_C_NEC_otus_data_heatmap,P11_C_NEC_color_dict,title)
plot_cluster_heatmap(P11_C_NEC_otus_data_heatmap,P11_C_NEC_color_dict,P11_C_NEC_row_colors,title)
No description has been provided for this image

P11_NEC_Non-NEC_TI¶

Load datasets¶

In [ ]:
# Load data
P11_C_NEC_otus_data_TI = pd.read_csv('./P11_NEC_Non-NEC/TI/tsv/data_normalized.csv',sep=',',index_col=0)
P11_C_NEC_metadata_TI = pd.read_csv('./P11_NEC_Non-NEC/TI/tsv/NEC_TI.csv',sep=',',index_col=0)
P11_C_NEC_otus_data_TI.head(10)
Out[ ]:
050B 051B 052B 053B 054B 055B 056B 057B 058B 059B ... 239B 240B 241B 242B 243B 244B 245B 246B 247B 248B
Lactobacillus 2.894527e+06 4.794247e+05 4.746304e+06 1.178586e+05 4.135038e+05 3.835398e+05 7.391131e+04 1.238514e+05 3.671594e+06 6.947663e+06 ... 7.277267e+06 2.299241e+06 1.258490e+05 4.634439e+05 165801.03880 2.317219e+05 2.077507e+05 153815.42150 2.634838e+06 3.755493e+05
Escherichia_Shigella 6.771874e+06 8.419896e+06 4.662405e+06 3.266081e+06 4.622453e+06 5.113863e+05 6.949660e+06 5.025969e+06 5.063923e+06 1.965641e+06 ... 1.797843e+04 1.278466e+05 1.140631e+06 5.992809e+04 203755.49340 2.477028e+05 3.076308e+06 521374.35080 8.030364e+05 1.977627e+05
Muribacter 9.188973e+04 0.000000e+00 1.997603e+05 2.796644e+04 2.397123e+04 8.729525e+05 3.935278e+05 1.598082e+05 5.193767e+04 6.272473e+05 ... 1.254495e+06 8.030364e+05 0.000000e+00 0.000000e+00 37954.45465 6.991610e+04 5.393528e+04 485417.49900 5.493408e+05 3.276069e+05
Staphylococcus 0.000000e+00 3.795445e+04 0.000000e+00 1.677986e+05 2.592889e+06 1.779864e+06 9.908110e+05 3.795445e+05 1.398322e+04 2.197363e+04 ... 2.796644e+04 5.593288e+04 1.258490e+06 1.508190e+06 517379.14500 2.113464e+06 2.087495e+06 685177.78670 7.471035e+05 2.087495e+06
Streptococcus 9.188973e+04 5.393528e+04 2.357171e+05 4.794247e+04 9.988014e+04 6.352377e+05 2.796644e+04 2.816620e+05 6.012785e+05 2.916500e+05 ... 2.696764e+05 2.996404e+04 2.097483e+05 1.737915e+05 27966.44027 2.237315e+05 1.458250e+05 0.00000 1.058730e+05 1.957651e+05
Klebsiella 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 0.000000e+00 8.989213e+04 8.589692e+04 6.991610e+04 135836.99560 2.596884e+04 4.994007e+04 65920.89493 3.196165e+04 7.191370e+04
Enterococcus 0.000000e+00 2.077507e+05 0.000000e+00 4.414702e+05 7.770675e+05 1.118658e+05 4.634439e+05 4.794247e+04 0.000000e+00 0.000000e+00 ... 0.000000e+00 5.793048e+04 2.491011e+06 3.915302e+05 163803.43590 7.331203e+05 5.972833e+05 97882.54095 9.268877e+05 3.855374e+05
Pseudomonas 9.788254e+04 5.173791e+05 9.788254e+04 3.449860e+06 7.950459e+05 2.754694e+06 5.693168e+05 2.147423e+06 2.656812e+05 6.392329e+04 ... 1.158610e+05 6.771874e+05 3.995206e+05 7.530963e+05 689172.99240 5.633240e+05 5.353576e+05 583300.04000 3.116260e+05 4.554535e+05
Lachnospiraceae_NK4A136_group 0.000000e+00 1.598082e+04 0.000000e+00 0.000000e+00 1.218538e+05 0.000000e+00 2.197363e+04 0.000000e+00 0.000000e+00 0.000000e+00 ... 0.000000e+00 2.996404e+04 3.995206e+04 1.797843e+04 189772.27330 4.994007e+04 2.996404e+04 43947.26328 1.358370e+05 6.592089e+04
Muribaculaceae 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 0.000000e+00 5.393528e+04 1.797843e+04 4.194966e+04 27966.44027 2.397123e+04 0.000000e+00 23971.23452 1.997603e+03 1.797843e+04

10 rows × 93 columns

Sort Metadata File¶

In [ ]:
P11_C_NEC_metadata_TI = sort_meta(P11_C_NEC_metadata_TI)
P11_C_NEC_metadata_TI.head(10)
Out[ ]:
Experiment
050B C
085B C
086B C
087B C
088B C
095B C
097B C
102B C
103B C
104B C

Build color dictionary¶

In [ ]:
P11_C_NEC_color_dict_TI,P11_C_NEC_row_colors_TI = colordict(P11_C_NEC_metadata_TI)
P11_C_NEC_color_dict_TI
Out[ ]:
{'C': 'g', 'NEC': 'blue'}

Build Heatmap Matrix¶

In [ ]:
P11_C_NEC_otus_data_heatmap_TI = create_heatmap(P11_C_NEC_otus_data_TI,P11_C_NEC_metadata_TI)
P11_C_NEC_otus_data_heatmap_TI.head(10)
Out[ ]:
050B 085B 086B 087B 088B 095B 097B 102B 103B 104B ... 072B 071B 070B 069B 068B 217B 218B 220B 099B 248B
Lactobacillus 2.894527e+06 6.935677e+06 7.375150e+06 9.125050e+06 8.162205e+06 5.463444e+06 6.835797e+06 6.817819e+06 4.864163e+06 7.878546e+06 ... 5.273672e+05 4.093088e+06 1.941670e+06 1.717938e+05 1.038753e+05 529364.76230 2.217339e+05 1.198562e+05 5.513384e+05 3.755493e+05
Escherichia_Shigella 6.771874e+06 3.995206e+03 2.121454e+06 0.000000e+00 9.988014e+03 2.207351e+06 0.000000e+00 7.990412e+03 0.000000e+00 3.455853e+05 ... 1.598082e+04 5.992809e+03 2.397123e+04 0.000000e+00 6.991610e+04 113863.36400 1.278466e+05 1.937675e+05 4.914103e+06 1.977627e+05
Muribacter 9.188973e+04 2.712745e+06 5.992809e+04 4.135038e+05 7.331203e+05 4.874151e+05 6.252497e+05 2.586896e+06 3.066320e+06 6.811826e+05 ... 6.572113e+05 9.588494e+04 1.638034e+05 8.050340e+05 7.351179e+05 962844.58650 4.854175e+05 1.310427e+06 0.000000e+00 3.276069e+05
Staphylococcus 0.000000e+00 5.193767e+04 1.997603e+03 9.988014e+03 9.988014e+03 2.197363e+04 4.594487e+04 0.000000e+00 0.000000e+00 0.000000e+00 ... 1.971634e+06 2.952457e+06 7.071514e+05 4.424690e+06 8.469836e+05 273671.59410 4.832201e+06 1.140631e+06 4.202956e+06 2.087495e+06
Streptococcus 9.188973e+04 2.996404e+04 2.576908e+05 2.177387e+05 7.111466e+05 5.793048e+04 5.393528e+04 1.658010e+05 4.354774e+05 4.314822e+05 ... 1.502197e+06 1.658010e+05 1.578106e+05 5.952857e+05 3.355973e+05 115860.96680 1.118658e+05 8.989213e+04 2.397123e+04 1.957651e+05
Klebsiella 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.516181e+06 2.393128e+06 0.000000e+00 1.040751e+06 0.000000e+00 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 133839.39270 4.394726e+04 7.191370e+04 5.992809e+03 7.191370e+04
Enterococcus 0.000000e+00 0.000000e+00 1.997603e+03 0.000000e+00 0.000000e+00 9.988014e+03 0.000000e+00 0.000000e+00 1.398322e+04 2.457052e+05 ... 1.260487e+06 1.186576e+06 5.946864e+06 2.848582e+06 4.646424e+06 69916.10068 1.917699e+05 7.990412e+04 1.997603e+04 3.855374e+05
Pseudomonas 9.788254e+04 9.788254e+04 6.192569e+04 6.592089e+04 1.338394e+05 3.995206e+04 1.598082e+04 2.277267e+05 3.795445e+05 1.598082e+04 ... 1.622054e+06 4.115062e+05 5.753096e+05 5.033959e+05 1.823811e+06 383539.75230 2.477028e+05 1.837795e+05 5.393528e+04 4.554535e+05
Lachnospiraceae_NK4A136_group 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.458250e+05 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 59928.08630 0.000000e+00 7.191370e+04 0.000000e+00 6.592089e+04
Muribaculaceae 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 17978.42589 2.197363e+04 3.595685e+04 0.000000e+00 1.797843e+04

10 rows × 93 columns

In [ ]:
title = "P11 Control vs NEC TI (Genus)"
# plot_cluster_heatmap(P11_C_NEC_otus_data_heatmap,P11_C_NEC_color_dict,title)
plot_cluster_heatmap(P11_C_NEC_otus_data_heatmap_TI,P11_C_NEC_color_dict_TI,P11_C_NEC_row_colors_TI,title)
/home/xuan/miniconda3/envs/xuan_cuda/lib/python3.10/site-packages/seaborn/matrix.py:560: UserWarning: Clustering large matrix with scipy. Installing `fastcluster` may give better performance.
  warnings.warn(msg)
No description has been provided for this image

P8 CONTROL WT HOMO HET¶

In [ ]:
# Load data
P8_C_gt_otus_data = pd.read_csv('Different_Time_Points/P8/CONTROL_WT_HOMO_HET/TSV/data_normalized.txt',sep='\t',index_col=0)
P8_C_gt_metadata = pd.read_csv('Different_Time_Points/P8/CONTROL_WT_HOMO_HET/TSV/P8_C_H_H.txt',sep='\t',index_col=0)
P8_C_gt_otus_data.head(10)
Out[ ]:
1 2 3 7 8 11 13 14 15 22 ... 25 30 40 41 42 43 46 47 48 49
Lactobacillus 8.900945e+06 8.575440e+06 8.748320e+06 8.940053e+06 8.764565e+06 6.213674e+06 5.237360e+06 5.450352e+06 5.784481e+06 5.553439e+06 ... 6.827781e+06 6.886946e+06 5.275065e+06 7.159905e+06 6.710455e+06 5.290708e+06 5.419266e+06 5.249794e+06 8.040753e+06 7.526123e+06
Escherichia_Shigella 1.363791e+04 1.444014e+04 9.626762e+03 1.562343e+05 1.183290e+04 3.427729e+06 4.378974e+06 4.289124e+06 3.975452e+06 4.078338e+06 ... 2.948597e+06 2.859550e+06 4.323419e+06 1.943603e+06 2.960029e+06 4.365937e+06 4.150739e+06 4.366539e+06 5.709873e+05 1.739035e+06
Muribacter 1.648583e+05 1.335713e+05 1.750867e+05 1.353763e+05 2.791761e+05 4.151541e+04 1.540282e+05 4.993883e+04 6.277451e+04 1.333708e+05 ... 2.226189e+04 7.420629e+04 3.890816e+04 9.105313e+04 4.893604e+04 3.369367e+04 7.520908e+04 7.741521e+04 3.299172e+05 1.658611e+05
Staphylococcus 1.774934e+05 4.346082e+05 3.457612e+05 2.206133e+03 2.206133e+03 1.664628e+04 4.672991e+04 1.022844e+04 1.203345e+04 5.013939e+03 ... 2.807806e+03 0.000000e+00 3.208921e+03 2.607248e+03 3.610036e+03 3.409478e+03 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
Streptococcus 5.653717e+05 5.888370e+05 5.298730e+05 5.780069e+05 7.546981e+05 9.747097e+04 7.440685e+04 5.655723e+04 7.139849e+04 1.076994e+05 ... 1.109083e+05 1.472092e+05 2.310423e+05 2.807806e+05 1.768918e+05 2.081787e+05 2.246245e+05 2.252261e+05 8.944867e+05 4.113435e+05
Enterococcus 9.827320e+03 2.206133e+03 1.002788e+03 3.610036e+03 3.208921e+03 1.403903e+03 1.203345e+03 1.002788e+03 1.805018e+03 0.000000e+00 ... 8.022302e+02 0.000000e+00 1.604460e+03 5.013939e+03 6.217284e+03 1.363791e+04 1.203345e+03 1.805018e+03 1.805018e+03 2.607248e+03
Lachnospiraceae_NK4A136_group 5.214496e+03 1.123122e+04 1.303624e+04 1.203345e+04 1.223401e+04 8.423417e+03 7.420629e+03 1.383847e+04 4.011151e+03 5.615611e+03 ... 4.412266e+03 1.403903e+03 7.621187e+03 2.807806e+04 5.214496e+03 6.217284e+03 1.022844e+04 6.016726e+02 1.383847e+04 9.225647e+03
Muribaculaceae 3.028419e+04 5.976615e+04 3.389423e+04 4.552656e+04 3.790538e+04 5.715890e+04 3.670203e+04 2.226189e+04 2.386635e+04 2.186077e+04 ... 2.045687e+04 4.211709e+03 3.168809e+04 1.293596e+05 1.865185e+04 2.527025e+04 2.466858e+04 2.286356e+04 4.131486e+04 4.512545e+04
Lachnospiraceae 1.002788e+03 5.214496e+03 2.807806e+03 5.214496e+03 1.805018e+03 2.807806e+03 1.203345e+03 6.016726e+02 2.607248e+03 2.206133e+03 ... 0.000000e+00 0.000000e+00 2.406691e+03 1.203345e+04 2.005575e+03 2.807806e+03 3.810593e+03 2.807806e+03 3.208921e+03 1.805018e+03
Bacteroides 7.821744e+03 7.220072e+03 9.426205e+03 6.417842e+03 5.615611e+03 7.821744e+03 3.610036e+03 7.019514e+03 2.206133e+03 6.818957e+03 ... 3.008363e+03 0.000000e+00 5.013939e+03 1.383847e+04 2.807806e+03 1.805018e+03 1.203345e+04 4.011151e+03 6.818957e+03 5.013939e+03

10 rows × 22 columns

In [ ]:
def format_meta(metadata,condition ='Experiment'):
    metadata[condition] = metadata[condition].str.rstrip()
    return metadata
P8_C_gt_metadata = format_meta(P8_C_gt_metadata,"Genotype")
In [ ]:
P8_C_gt_metadata.head(10)
Out[ ]:
Experiment Tissue type DOL Genotype
1 C colon DOL8 Sig Homo
2 C colon DOL8 Sig WT
3 C colon DOL8 Sig Het
7 C colon DOL8 Sig Homo
8 C colon DOL8 Sig Homo
11 C colon DOL8 Sig WT
12 C colon DOL8 Sig Het
13 C colon DOL8 Sig Homo
14 C colon DOL8 Sig Het
15 C colon DOL8 Sig Het
In [ ]:
def genotypecolordict(metadata,condition ='Experiment'):
    # metadata = metadata.sort_values(condition,ascending=False)
    # metadata[condition] = metadata[condition].str.replace(' ', '')
    color_dict=dict(zip(np.unique(metadata[condition]),np.array(['g','blue','violet'])))
    # print(metadata[condition])
    row_colors = metadata[condition].map(color_dict)
    # print(metadata.index)
    return color_dict,row_colors
In [ ]:
def sort_meta_GT(P8_C_F_metadata,condition = "Experiment"):
    new_meta_index = []
    for index1 in P8_C_F_metadata.index:
        new_meta_index.append(str(index1))
    P8_C_F_metadata.index = new_meta_index
    # metadata = P8_C_F_metadata.sort_values(condition,ascending=False)
    # metadata = P8_C_F_metadata.sort_values(condition,ascending=False)
    return P8_C_F_metadata
P8_C_metadata_gt= sort_meta_GT(P8_C_gt_metadata,"Genotype")
P8_C_metadata_gt.head(5)
Out[ ]:
Experiment Tissue type DOL Genotype
1 C colon DOL8 Sig Homo
2 C colon DOL8 Sig WT
3 C colon DOL8 Sig Het
7 C colon DOL8 Sig Homo
8 C colon DOL8 Sig Homo
In [ ]:
def create_heatmap(otus_data,metadata,condition ="Experiment" ):
    heatmap = otus_data
    otus_data = otus_data.transpose()
    new_column = []
    new_idx = []
    for index1 in otus_data.index:
        new_idx.append(str(index1).rstrip())
    otus_data.index = new_idx
    # print(new_idx)
    for index1 in otus_data.index:
        for index2 in metadata.index:
            value = metadata.loc[index2, condition]
            if str(index1) == str(index2):
                new_column.append(value)
    # print(new_column)
    otus_data[condition] = new_column
    # print(otus_data[condition])
    otus_data = otus_data.sort_values(by=condition,ascending=False)
    # print(otus_data[condition])
    heatmap = otus_data.drop(columns=[condition])
    heatmap = heatmap.transpose()
    return heatmap
P8_C_otus_data_heatmap = create_heatmap(P8_C_gt_otus_data,P8_C_metadata_gt,"Genotype")
P8_C_otus_data_heatmap.head(5)
Out[ ]:
2 46 11 40 22 25 1 47 42 41 ... 23 13 8 7 15 14 43 3 48 49
Lactobacillus 8.575440e+06 5.419266e+06 6.213674e+06 5.275065e+06 5.553439e+06 6.827781e+06 8.900945e+06 5.249794e+06 6.710455e+06 7.159905e+06 ... 6.614589e+06 5.237360e+06 8.764565e+06 8.940053e+06 5.784481e+06 5.450352e+06 5.290708e+06 8.748320e+06 8.040753e+06 7.526123e+06
Escherichia_Shigella 1.444014e+04 4.150739e+06 3.427729e+06 4.323419e+06 4.078338e+06 2.948597e+06 1.363791e+04 4.366539e+06 2.960029e+06 1.943603e+06 ... 3.074547e+06 4.378974e+06 1.183290e+04 1.562343e+05 3.975452e+06 4.289124e+06 4.365937e+06 9.626762e+03 5.709873e+05 1.739035e+06
Muribacter 1.335713e+05 7.520908e+04 4.151541e+04 3.890816e+04 1.333708e+05 2.226189e+04 1.648583e+05 7.741521e+04 4.893604e+04 9.105313e+04 ... 4.512545e+04 1.540282e+05 2.791761e+05 1.353763e+05 6.277451e+04 4.993883e+04 3.369367e+04 1.750867e+05 3.299172e+05 1.658611e+05
Staphylococcus 4.346082e+05 0.000000e+00 1.664628e+04 3.208921e+03 5.013939e+03 2.807806e+03 1.774934e+05 0.000000e+00 3.610036e+03 2.607248e+03 ... 1.203345e+03 4.672991e+04 2.206133e+03 2.206133e+03 1.203345e+04 1.022844e+04 3.409478e+03 3.457612e+05 0.000000e+00 0.000000e+00
Streptococcus 5.888370e+05 2.246245e+05 9.747097e+04 2.310423e+05 1.076994e+05 1.109083e+05 5.653717e+05 2.252261e+05 1.768918e+05 2.807806e+05 ... 1.895269e+05 7.440685e+04 7.546981e+05 5.780069e+05 7.139849e+04 5.655723e+04 2.081787e+05 5.298730e+05 8.944867e+05 4.113435e+05

5 rows × 22 columns

In [ ]:
def plot_cluster_heatmap(heatmap,color_dict,row_colors,title):
    # print(row_colors.index)
    custom_cmap = sns.color_palette("OrRd", as_cmap=True)
    hm = sns.clustermap(heatmap,
                metric="correlation",
                standard_scale=0,
                z_score=None,
                col_colors=row_colors,
                col_cluster=False,
                cmap=custom_cmap,
                # cbar_pos=(0, .2, .03, .4),
                figsize=(14, 12))
    # Create a color legend using the color_dict
    legend_labels = [f"{experiment}" for experiment, color in color_dict.items()]
    legend_colors = [color for _, color in color_dict.items()]
    # print(legend_colors)
    legend_handles = [plt.Line2D([0], [0], marker='o', color='w', label=label, markersize=10, markerfacecolor=color) for label, color in zip(legend_labels, legend_colors)]
    plt.legend(handles=legend_handles, title="Genotype", bbox_to_anchor=(15, 1), loc='upper left')
    # Add a title to the center of the heatmap
    ax  = hm.ax_heatmap
    ax.text(0.5, 1.1, title, fontsize=12, ha="center", va="center", transform=ax.transAxes)
    # Get the current Axes objects
    ax_row_labels = hm.ax_row_dendrogram
    ax_col_labels = hm.ax_col_dendrogram

    # Set row and column labels font size
    row_font_size = 4
    col_font_size = 4

    for label in ax_row_labels.get_yticklabels():
        label.set_fontsize(row_font_size)

    for label in ax_col_labels.get_xticklabels():
        label.set_fontsize(col_font_size)

    # Display the plot
    plt.show()
title = "P8 Control Genotype WT vs Homo vs Het"
P8_C_color_dict_gt,P8_C_row_colors_gt = genotypecolordict(P8_C_gt_metadata,"Genotype")
plot_cluster_heatmap(P8_C_otus_data_heatmap,P8_C_color_dict_gt,P8_C_row_colors_gt,title)
No description has been provided for this image

P8 CONTROL WT HOMO HET¶

In [ ]:
# Load data
P8_C_gt_hh = pd.read_csv('Different_Time_Points/P8/CONTROL_WT_HOMOHET/TSV/data_normalized.txt',sep='\t',index_col=0)
P8_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P8/CONTROL_WT_HOMOHET/TSV/P8_C_Het+Homo.txt',sep='\t',index_col=0)
P8_C_gt_hh.head(10)
Out[ ]:
1 2 3 7 8 11 13 14 15 22 ... 25 30 40 41 42 43 46 47 48 49
Lactobacillus 8.900945e+06 8.575440e+06 8.748320e+06 8.940053e+06 8.764565e+06 6.213674e+06 5.237360e+06 5.450352e+06 5.784481e+06 5.553439e+06 ... 6.827781e+06 6.886946e+06 5.275065e+06 7.159905e+06 6.710455e+06 5.290708e+06 5.419266e+06 5.249794e+06 8.040753e+06 7.526123e+06
Escherichia_Shigella 1.363791e+04 1.444014e+04 9.626762e+03 1.562343e+05 1.183290e+04 3.427729e+06 4.378974e+06 4.289124e+06 3.975452e+06 4.078338e+06 ... 2.948597e+06 2.859550e+06 4.323419e+06 1.943603e+06 2.960029e+06 4.365937e+06 4.150739e+06 4.366539e+06 5.709873e+05 1.739035e+06
Muribacter 1.648583e+05 1.335713e+05 1.750867e+05 1.353763e+05 2.791761e+05 4.151541e+04 1.540282e+05 4.993883e+04 6.277451e+04 1.333708e+05 ... 2.226189e+04 7.420629e+04 3.890816e+04 9.105313e+04 4.893604e+04 3.369367e+04 7.520908e+04 7.741521e+04 3.299172e+05 1.658611e+05
Staphylococcus 1.774934e+05 4.346082e+05 3.457612e+05 2.206133e+03 2.206133e+03 1.664628e+04 4.672991e+04 1.022844e+04 1.203345e+04 5.013939e+03 ... 2.807806e+03 0.000000e+00 3.208921e+03 2.607248e+03 3.610036e+03 3.409478e+03 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
Streptococcus 5.653717e+05 5.888370e+05 5.298730e+05 5.780069e+05 7.546981e+05 9.747097e+04 7.440685e+04 5.655723e+04 7.139849e+04 1.076994e+05 ... 1.109083e+05 1.472092e+05 2.310423e+05 2.807806e+05 1.768918e+05 2.081787e+05 2.246245e+05 2.252261e+05 8.944867e+05 4.113435e+05
Enterococcus 9.827320e+03 2.206133e+03 1.002788e+03 3.610036e+03 3.208921e+03 1.403903e+03 1.203345e+03 1.002788e+03 1.805018e+03 0.000000e+00 ... 8.022302e+02 0.000000e+00 1.604460e+03 5.013939e+03 6.217284e+03 1.363791e+04 1.203345e+03 1.805018e+03 1.805018e+03 2.607248e+03
Lachnospiraceae_NK4A136_group 5.214496e+03 1.123122e+04 1.303624e+04 1.203345e+04 1.223401e+04 8.423417e+03 7.420629e+03 1.383847e+04 4.011151e+03 5.615611e+03 ... 4.412266e+03 1.403903e+03 7.621187e+03 2.807806e+04 5.214496e+03 6.217284e+03 1.022844e+04 6.016726e+02 1.383847e+04 9.225647e+03
Muribaculaceae 3.028419e+04 5.976615e+04 3.389423e+04 4.552656e+04 3.790538e+04 5.715890e+04 3.670203e+04 2.226189e+04 2.386635e+04 2.186077e+04 ... 2.045687e+04 4.211709e+03 3.168809e+04 1.293596e+05 1.865185e+04 2.527025e+04 2.466858e+04 2.286356e+04 4.131486e+04 4.512545e+04
Lachnospiraceae 1.002788e+03 5.214496e+03 2.807806e+03 5.214496e+03 1.805018e+03 2.807806e+03 1.203345e+03 6.016726e+02 2.607248e+03 2.206133e+03 ... 0.000000e+00 0.000000e+00 2.406691e+03 1.203345e+04 2.005575e+03 2.807806e+03 3.810593e+03 2.807806e+03 3.208921e+03 1.805018e+03
Bacteroides 7.821744e+03 7.220072e+03 9.426205e+03 6.417842e+03 5.615611e+03 7.821744e+03 3.610036e+03 7.019514e+03 2.206133e+03 6.818957e+03 ... 3.008363e+03 0.000000e+00 5.013939e+03 1.383847e+04 2.807806e+03 1.805018e+03 1.203345e+04 4.011151e+03 6.818957e+03 5.013939e+03

10 rows × 22 columns

In [ ]:
P8_C_gt_metadata_hh = format_meta(P8_C_gt_metadata_hh,"Genotype")
In [ ]:
P8_C_metadata_gt_hh= sort_meta_GT(P8_C_gt_metadata_hh,"Genotype")
P8_C_metadata_gt_hh.head(5)
Out[ ]:
Experiment Tissue type DOL Genotype
1 C colon DOL8 Sig Homo_HET
2 C colon DOL8 Sig WT
3 C colon DOL8 Sig Homo_HET
7 C colon DOL8 Sig Homo_HET
8 C colon DOL8 Sig Homo_HET
In [ ]:
P8_C_hh_data_heatmap = create_heatmap(P8_C_gt_otus_data,P8_C_metadata_gt,"Genotype")
P8_C_hh_data_heatmap.head(5)
Out[ ]:
2 46 11 40 22 25 1 47 42 41 ... 23 13 8 7 15 14 43 3 48 49
Lactobacillus 8.575440e+06 5.419266e+06 6.213674e+06 5.275065e+06 5.553439e+06 6.827781e+06 8.900945e+06 5.249794e+06 6.710455e+06 7.159905e+06 ... 6.614589e+06 5.237360e+06 8.764565e+06 8.940053e+06 5.784481e+06 5.450352e+06 5.290708e+06 8.748320e+06 8.040753e+06 7.526123e+06
Escherichia_Shigella 1.444014e+04 4.150739e+06 3.427729e+06 4.323419e+06 4.078338e+06 2.948597e+06 1.363791e+04 4.366539e+06 2.960029e+06 1.943603e+06 ... 3.074547e+06 4.378974e+06 1.183290e+04 1.562343e+05 3.975452e+06 4.289124e+06 4.365937e+06 9.626762e+03 5.709873e+05 1.739035e+06
Muribacter 1.335713e+05 7.520908e+04 4.151541e+04 3.890816e+04 1.333708e+05 2.226189e+04 1.648583e+05 7.741521e+04 4.893604e+04 9.105313e+04 ... 4.512545e+04 1.540282e+05 2.791761e+05 1.353763e+05 6.277451e+04 4.993883e+04 3.369367e+04 1.750867e+05 3.299172e+05 1.658611e+05
Staphylococcus 4.346082e+05 0.000000e+00 1.664628e+04 3.208921e+03 5.013939e+03 2.807806e+03 1.774934e+05 0.000000e+00 3.610036e+03 2.607248e+03 ... 1.203345e+03 4.672991e+04 2.206133e+03 2.206133e+03 1.203345e+04 1.022844e+04 3.409478e+03 3.457612e+05 0.000000e+00 0.000000e+00
Streptococcus 5.888370e+05 2.246245e+05 9.747097e+04 2.310423e+05 1.076994e+05 1.109083e+05 5.653717e+05 2.252261e+05 1.768918e+05 2.807806e+05 ... 1.895269e+05 7.440685e+04 7.546981e+05 5.780069e+05 7.139849e+04 5.655723e+04 2.081787e+05 5.298730e+05 8.944867e+05 4.113435e+05

5 rows × 22 columns

In [ ]:
title = "P8 Control Genotype WT vs Homo_HET"
P8_C_color_dict_gt_hh,P8_C_row_colors_gt_hh = genotypecolordict(P8_C_metadata_gt_hh,"Genotype")
plot_cluster_heatmap(P8_C_hh_data_heatmap,P8_C_color_dict_gt_hh,P8_C_row_colors_gt_hh,title)
No description has been provided for this image

P8 Feeding WT HOMO HET¶

In [ ]:
# Load data
P8_F_gt_h_h = pd.read_csv('Different_Time_Points/P8/FEEDING_WT_HOMO_HET/TSV/data_normalized.txt',sep='\t',index_col=0)
P8_F_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P8/FEEDING_WT_HOMO_HET/TSV/P8_F_H_H.txt',sep='\t',index_col=0)
P8_F_gt_h_h.head(10)
Out[ ]:
4 5 6 9 10 16 17 18 19 20 ... 32 33 34 35 36 37 38 39 44 45
Lactobacillus 2.757596e+05 7.052214e+05 2.520262e+05 1.469857e+06 5.145468e+05 4.352740e+05 1.942265e+05 1.465013e+06 5.038910e+05 3.233556e+06 ... 2.221576e+06 5.631438e+05 8.363202e+04 4.975136e+06 5.505667e+06 4.188059e+05 7.666538e+06 7.145210e+06 3.528206e+06 7.570474e+05
Escherichia_Shigella 8.785398e+06 7.533017e+06 7.648132e+06 7.174271e+06 7.895476e+06 9.028545e+06 8.464594e+06 8.180116e+06 8.602958e+06 5.924634e+06 ... 7.336046e+06 8.683038e+06 8.799445e+06 2.421777e+04 1.291614e+04 1.002616e+05 1.501502e+04 1.727534e+04 5.471116e+06 8.344796e+06
Muribacter 7.265330e+03 2.723691e+05 8.347057e+04 5.392489e+04 2.216733e+05 1.372340e+04 1.291614e+04 1.227033e+04 3.180600e+04 5.973716e+03 ... 1.517647e+04 4.843553e+03 2.906132e+04 2.269528e+06 9.530498e+05 5.574607e+06 4.084730e+04 6.120637e+05 1.356195e+04 1.888986e+04
Staphylococcus 1.414318e+05 8.761988e+05 7.265330e+04 7.593077e+05 1.004069e+06 8.411637e+04 6.813265e+04 1.248022e+05 1.906745e+05 1.018761e+05 ... 1.937421e+03 5.005005e+03 0.000000e+00 3.137008e+05 1.653751e+06 1.076883e+05 7.234654e+05 1.888986e+05 2.599374e+04 1.243179e+04
Streptococcus 6.780975e+03 5.553941e+04 2.502503e+04 2.437922e+04 8.944428e+04 1.033291e+04 2.437922e+04 2.550938e+04 2.599374e+04 2.276470e+04 ... 8.718396e+03 1.146308e+04 8.072589e+02 4.903290e+05 6.445155e+05 4.294617e+04 1.438535e+05 4.062127e+05 1.855081e+05 7.555943e+04
Enterococcus 1.388485e+04 5.489360e+03 3.600375e+04 1.178598e+04 8.556944e+03 5.166457e+03 1.210888e+04 2.906132e+03 6.619523e+03 4.197746e+03 ... 4.682101e+03 7.265330e+03 1.775970e+04 3.342052e+04 1.372340e+04 3.923278e+04 2.357196e+04 2.405631e+04 8.072589e+03 1.049437e+04
Pseudomonas 0.000000e+00 0.000000e+00 1.453066e+03 0.000000e+00 4.843553e+02 0.000000e+00 1.453066e+03 0.000000e+00 0.000000e+00 0.000000e+00 ... 3.229035e+02 3.229035e+03 0.000000e+00 4.359198e+03 1.291614e+03 3.229035e+03 2.260325e+03 0.000000e+00 1.614518e+03 4.843553e+02
Lachnospiraceae_NK4A136_group 4.262327e+04 2.776971e+04 1.076883e+05 3.212890e+04 1.291614e+04 2.179599e+04 6.215893e+04 9.364203e+03 2.954567e+04 4.343053e+04 ... 1.840550e+04 3.487358e+04 5.666957e+04 9.574090e+04 6.054442e+04 1.993929e+05 7.055443e+04 7.507508e+04 4.100875e+04 4.601376e+04
Muribaculaceae 1.879299e+05 1.356195e+05 4.259098e+05 1.407859e+05 6.684103e+04 9.477219e+04 2.881914e+05 4.601376e+04 1.396558e+05 1.587071e+05 ... 8.702251e+04 1.779199e+05 2.973942e+05 4.654655e+05 2.161839e+05 7.723853e+05 3.153153e+05 4.063741e+05 1.948723e+05 1.829249e+05
Lachnospiraceae 1.953566e+04 8.234040e+03 4.795118e+04 9.687106e+03 2.583228e+03 9.848558e+03 3.777972e+04 2.260325e+03 1.888986e+04 1.566082e+04 ... 8.718396e+03 2.098873e+04 3.358197e+04 4.730537e+04 2.825406e+04 1.273854e+05 3.745681e+04 4.536795e+04 2.034292e+04 2.615519e+04

10 rows × 26 columns

In [ ]:
P8_F_gt_metadata_h_h = format_meta(P8_F_gt_metadata_h_h,"Genotype")
In [ ]:
P8_F_gt_metadata_h_h.head(5)
Out[ ]:
Experiment Tissue type DOL Genotype
4 F colon DOL8 Sig Homo
5 F colon DOL8 Sig WT
6 F colon DOL8 Sig HET
9 F colon DOL8 Sig HET
10 F colon DOL8 Sig WT
In [ ]:
P8_F_metadata_gt_h_h= sort_meta_GT(P8_F_gt_metadata_h_h,"Genotype")
P8_F_metadata_gt_h_h.head(5)
Out[ ]:
Experiment Tissue type DOL Genotype
4 F colon DOL8 Sig Homo
5 F colon DOL8 Sig WT
6 F colon DOL8 Sig HET
9 F colon DOL8 Sig HET
10 F colon DOL8 Sig WT
In [ ]:
P8_F_h_h_data_heatmap = create_heatmap(P8_F_gt_h_h,P8_F_metadata_gt_h_h,"Genotype")
P8_F_h_h_data_heatmap.head(5)
Out[ ]:
27 44 39 10 36 18 32 20 5 4 ... 31 19 33 34 35 17 38 9 6 28
Lactobacillus 3.677871e+05 3.528206e+06 7.145210e+06 5.145468e+05 5.505667e+06 1.465013e+06 2.221576e+06 3.233556e+06 7.052214e+05 2.757596e+05 ... 4.552940e+04 5.038910e+05 5.631438e+05 8.363202e+04 4.975136e+06 1.942265e+05 7.666538e+06 1.469857e+06 2.520262e+05 1.468888e+06
Escherichia_Shigella 8.919403e+06 5.471116e+06 1.727534e+04 7.895476e+06 1.291614e+04 8.180116e+06 7.336046e+06 5.924634e+06 7.533017e+06 8.785398e+06 ... 9.122510e+06 8.602958e+06 8.683038e+06 8.799445e+06 2.421777e+04 8.464594e+06 1.501502e+04 7.174271e+06 7.648132e+06 7.740644e+06
Muribacter 1.905131e+04 1.356195e+04 6.120637e+05 2.216733e+05 9.530498e+05 1.227033e+04 1.517647e+04 5.973716e+03 2.723691e+05 7.265330e+03 ... 5.327909e+03 3.180600e+04 4.843553e+03 2.906132e+04 2.269528e+06 1.291614e+04 4.084730e+04 5.392489e+04 8.347057e+04 1.614518e+03
Staphylococcus 1.650037e+05 2.599374e+04 1.888986e+05 1.004069e+06 1.653751e+06 1.248022e+05 1.937421e+03 1.018761e+05 8.761988e+05 1.414318e+05 ... 1.775970e+03 1.906745e+05 5.005005e+03 0.000000e+00 3.137008e+05 6.813265e+04 7.234654e+05 7.593077e+05 7.265330e+04 1.559624e+05
Streptococcus 1.727534e+04 1.855081e+05 4.062127e+05 8.944428e+04 6.445155e+05 2.550938e+04 8.718396e+03 2.276470e+04 5.553941e+04 6.780975e+03 ... 0.000000e+00 2.599374e+04 1.146308e+04 8.072589e+02 4.903290e+05 2.437922e+04 1.438535e+05 2.437922e+04 2.502503e+04 2.034292e+04

5 rows × 26 columns

In [ ]:
title = "P8 FEEDING Genotype WT vs Homo vs HET"
P8_F_color_dict_gt_h_h,P8_F_row_colors_gt_h_h = genotypecolordict(P8_F_metadata_gt_h_h,"Genotype")
plot_cluster_heatmap(P8_F_h_h_data_heatmap,P8_F_color_dict_gt_h_h,P8_F_row_colors_gt_h_h,title)
No description has been provided for this image

P8 Feeding WT HOMOHET¶

In [ ]:
# Load data
P8_F_gt_hh = pd.read_csv('Different_Time_Points/P8/FEEDING_WT_HOMOHET/TSV/data_normalized.txt',sep='\t',index_col=0)
P8_F_gt_metadata_hh = pd.read_csv('Different_Time_Points/P8/FEEDING_WT_HOMOHET/TSV/P8_F_Het+Homo.txt',sep='\t',index_col=0)
P8_F_gt_hh.head(5)
Out[ ]:
4 5 6 9 10 16 17 18 19 20 ... 32 33 34 35 36 37 38 39 44 45
Lactobacillus 2.757596e+05 7.052214e+05 2.520262e+05 1.469857e+06 5.145468e+05 4.352740e+05 1.942265e+05 1.465013e+06 5.038910e+05 3.233556e+06 ... 2.221576e+06 5.631438e+05 8.363202e+04 4.975136e+06 5.505667e+06 4.188059e+05 7.666538e+06 7.145210e+06 3.528206e+06 7.570474e+05
Escherichia_Shigella 8.785398e+06 7.533017e+06 7.648132e+06 7.174271e+06 7.895476e+06 9.028545e+06 8.464594e+06 8.180116e+06 8.602958e+06 5.924634e+06 ... 7.336046e+06 8.683038e+06 8.799445e+06 2.421777e+04 1.291614e+04 1.002616e+05 1.501502e+04 1.727534e+04 5.471116e+06 8.344796e+06
Muribacter 7.265330e+03 2.723691e+05 8.347057e+04 5.392489e+04 2.216733e+05 1.372340e+04 1.291614e+04 1.227033e+04 3.180600e+04 5.973716e+03 ... 1.517647e+04 4.843553e+03 2.906132e+04 2.269528e+06 9.530498e+05 5.574607e+06 4.084730e+04 6.120637e+05 1.356195e+04 1.888986e+04
Staphylococcus 1.414318e+05 8.761988e+05 7.265330e+04 7.593077e+05 1.004069e+06 8.411637e+04 6.813265e+04 1.248022e+05 1.906745e+05 1.018761e+05 ... 1.937421e+03 5.005005e+03 0.000000e+00 3.137008e+05 1.653751e+06 1.076883e+05 7.234654e+05 1.888986e+05 2.599374e+04 1.243179e+04
Streptococcus 6.780975e+03 5.553941e+04 2.502503e+04 2.437922e+04 8.944428e+04 1.033291e+04 2.437922e+04 2.550938e+04 2.599374e+04 2.276470e+04 ... 8.718396e+03 1.146308e+04 8.072589e+02 4.903290e+05 6.445155e+05 4.294617e+04 1.438535e+05 4.062127e+05 1.855081e+05 7.555943e+04

5 rows × 26 columns

In [ ]:
P8_F_gt_metadata_hh = format_meta(P8_F_gt_metadata_hh,"Genotype")
P8_F_gt_metadata_hh.head(5)
Out[ ]:
Experiment Tissue type DOL Genotype
4 F colon DOL8 Sig Homo_HET
5 F colon DOL8 Sig WT
6 F colon DOL8 Sig Homo_HET
9 F colon DOL8 Sig Homo_HET
10 F colon DOL8 Sig WT
In [ ]:
P8_F_gt_metadata_hh= sort_meta_GT(P8_F_gt_metadata_hh,"Genotype")
P8_F_gt_metadata_hh.head(5)
Out[ ]:
Experiment Tissue type DOL Genotype
4 F colon DOL8 Sig Homo_HET
5 F colon DOL8 Sig WT
6 F colon DOL8 Sig Homo_HET
9 F colon DOL8 Sig Homo_HET
10 F colon DOL8 Sig WT
In [ ]:
P8_F_hh_data_heatmap = create_heatmap(P8_F_gt_hh,P8_F_gt_metadata_hh,"Genotype")
P8_F_hh_data_heatmap.head(5)
Out[ ]:
32 44 39 10 18 36 20 27 5 4 ... 31 29 26 21 19 17 16 9 6 45
Lactobacillus 2.221576e+06 3.528206e+06 7.145210e+06 5.145468e+05 1.465013e+06 5.505667e+06 3.233556e+06 3.677871e+05 7.052214e+05 2.757596e+05 ... 4.552940e+04 2.356066e+06 9.685492e+05 1.649714e+06 5.038910e+05 1.942265e+05 4.352740e+05 1.469857e+06 2.520262e+05 7.570474e+05
Escherichia_Shigella 7.336046e+06 5.471116e+06 1.727534e+04 7.895476e+06 8.180116e+06 1.291614e+04 5.924634e+06 8.919403e+06 7.533017e+06 8.785398e+06 ... 9.122510e+06 7.086280e+06 7.507508e+06 7.093868e+06 8.602958e+06 8.464594e+06 9.028545e+06 7.174271e+06 7.648132e+06 8.344796e+06
Muribacter 1.517647e+04 1.356195e+04 6.120637e+05 2.216733e+05 1.227033e+04 9.530498e+05 5.973716e+03 1.905131e+04 2.723691e+05 7.265330e+03 ... 5.327909e+03 5.489360e+03 1.130162e+03 4.020149e+04 3.180600e+04 1.291614e+04 1.372340e+04 5.392489e+04 8.347057e+04 1.888986e+04
Staphylococcus 1.937421e+03 2.599374e+04 1.888986e+05 1.004069e+06 1.248022e+05 1.653751e+06 1.018761e+05 1.650037e+05 8.761988e+05 1.414318e+05 ... 1.775970e+03 1.630663e+05 1.243179e+04 2.986858e+04 1.906745e+05 6.813265e+04 8.411637e+04 7.593077e+05 7.265330e+04 1.243179e+04
Streptococcus 8.718396e+03 1.855081e+05 4.062127e+05 8.944428e+04 2.550938e+04 6.445155e+05 2.276470e+04 1.727534e+04 5.553941e+04 6.780975e+03 ... 0.000000e+00 4.004004e+04 1.291614e+03 5.327909e+03 2.599374e+04 2.437922e+04 1.033291e+04 2.437922e+04 2.502503e+04 7.555943e+04

5 rows × 26 columns

In [ ]:
title = "P8 FEEDING Genotype WT vs Homo_Het"
P8_F_color_dict_gt_hh,P8_F_row_colors_gt_hh = genotypecolordict(P8_F_gt_metadata_hh,"Genotype")
# print(P8_F_row_colors_gt_hh)
plot_cluster_heatmap(P8_F_hh_data_heatmap,P8_F_color_dict_gt_hh,P8_F_row_colors_gt_hh,title)
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P11 COLON WT HOMO HET CONTROL¶

In [ ]:
# Load data
P11_c_gt_h_h = pd.read_csv('Different_Time_Points/P11_COLON_WT_Homo_HET/CONTROL/TSV/data_normalized.txt',sep='\t',index_col=0)
P11_c_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P11_COLON_WT_Homo_HET/CONTROL/TSV/P11_COLON_WT_Homo_HET_C.txt',sep='\t',index_col=0)
P11_c_gt_metadata_h_h = format_meta(P11_c_gt_metadata_h_h,"Genotype")
P11_C_h_h_data_heatmap = create_heatmap(P11_c_gt_h_h,P11_c_gt_metadata_h_h,"Genotype")

title = "P11 COLON Genotype WT vs Homo vs Het CONTROL"
P11_C_color_dict_gt_h_h,P11_C_row_colors_gt_h_h = genotypecolordict(P11_c_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P11_C_h_h_data_heatmap,P11_C_color_dict_gt_h_h,P11_C_row_colors_gt_h_h,title)
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In [ ]:
# Load data
P11_NEC_gt_h_h = pd.read_csv('Different_Time_Points/P11_COLON_WT_Homo_HET/NEC/TSV/data_normalized.txt',sep='\t',index_col=0)
P11_NEC_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P11_COLON_WT_Homo_HET/NEC/TSV/P11_COLON_WT_Homo_HET_NEC.txt',sep='\t',index_col=0)
P11_NEC_gt_metadata_h_h = format_meta(P11_NEC_gt_metadata_h_h,"Genotype")
P11_NEC_h_h_data_heatmap = create_heatmap(P11_NEC_gt_h_h,P11_NEC_gt_metadata_h_h,"Genotype")

title = "P11 COLON Genotype WT vs Homo vs Het NEC"
P11_NEC_color_dict_gt_h_h,P11_NEC_row_colors_gt_h_h = genotypecolordict(P11_NEC_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P11_NEC_h_h_data_heatmap,P11_NEC_color_dict_gt_h_h,P11_NEC_row_colors_gt_h_h,title)
No description has been provided for this image
In [ ]:
# Load data
P11_TI_C_gt_h_h = pd.read_csv('Different_Time_Points/P11_Ileal_WT_Homo_HET/CONTROL/tsv/data_normalized.txt',sep='\t',index_col=0)
P11_TI_C_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P11_Ileal_WT_Homo_HET/CONTROL/tsv/P11_Ileal_WT_Homo_HET_C.txt',sep='\t',index_col=0)
P11_TI_C_gt_metadata_h_h = format_meta(P11_TI_C_gt_metadata_h_h,"Genotype")
P11_TI_C_h_h_data_heatmap = create_heatmap(P11_TI_C_gt_h_h,P11_TI_C_gt_metadata_h_h,"Genotype")

title = "P11 TI Genotype WT vs Homo vs Het Control"
P11_TI_C_color_dict_gt_h_h,P11_TI_C_row_colors_gt_h_h = genotypecolordict(P11_TI_C_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P11_TI_C_h_h_data_heatmap,P11_TI_C_color_dict_gt_h_h,P11_TI_C_row_colors_gt_h_h,title)
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In [ ]:
# Load data
P11_TI_NEC_gt_h_h = pd.read_csv('Different_Time_Points/P11_Ileal_WT_Homo_HET/NEC/tsv/data_normalized.txt',sep='\t',index_col=0)
P11_TI_NEC_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P11_Ileal_WT_Homo_HET/NEC/tsv/P11_Ileal_WT_Homo_HET_NEC.txt',sep='\t',index_col=0)
P11_TI_NEC_gt_metadata_h_h = format_meta(P11_TI_NEC_gt_metadata_h_h,"Genotype")
P11_TI_NEC_h_h_data_heatmap = create_heatmap(P11_TI_NEC_gt_h_h,P11_TI_NEC_gt_metadata_h_h,"Genotype")

title = "P11 TI Genotype WT vs Homo vs Het NEC"
P11_TI_NEC_color_dict_gt_h_h,P11_TI_NEC_row_colors_gt_h_h = genotypecolordict(P11_TI_NEC_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P11_TI_NEC_h_h_data_heatmap,P11_TI_NEC_color_dict_gt_h_h,P11_TI_NEC_row_colors_gt_h_h,title)
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In [ ]:
# Load data
P11_TI_C_gt_hh = pd.read_csv('Different_Time_Points/P11_Ileal_WT_HomoHET/CONTROL/tsv/data_normalized.txt',sep='\t',index_col=0)
P11_TI_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P11_Ileal_WT_HomoHET/CONTROL/tsv/P11_Ileal_WT_HomoHET_C.txt',sep='\t',index_col=0)
P11_TI_C_gt_metadata_hh = format_meta(P11_TI_C_gt_metadata_hh,"Genotype")
P11_TI_C_hh_data_heatmap = create_heatmap(P11_TI_C_gt_hh,P11_TI_C_gt_metadata_hh,"Genotype")

title = "P11 TI Genotype WT vs Homo_Het CONTROL"
P11_TI_C_color_dict_gt_hh,P11_TI_C_row_colors_gt_hh = genotypecolordict(P11_TI_C_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P11_TI_C_hh_data_heatmap,P11_TI_C_color_dict_gt_hh,P11_TI_C_row_colors_gt_hh,title)
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In [ ]:
# Load data
P11_TI_NEC_gt_hh = pd.read_csv('Different_Time_Points/P11_Ileal_WT_HomoHET/NEC/tsv/data_normalized.txt',sep='\t',index_col=0)
P11_TI_NEC_gt_metadata_hh = pd.read_csv('Different_Time_Points/P11_Ileal_WT_HomoHET/NEC/tsv/P11_Ileal_WT_HomoHET_NEC.txt',sep='\t',index_col=0)
P11_TI_NEC_gt_metadata_hh = format_meta(P11_TI_NEC_gt_metadata_hh,"Genotype")
P11_TI_NEC_hh_data_heatmap = create_heatmap(P11_TI_NEC_gt_hh,P11_TI_NEC_gt_metadata_hh,"Genotype")

title = "P11 TI Genotype WT vs Homo_Het NEC"
P11_TI_NEC_color_dict_gt_hh,P11_TI_NEC_row_colors_gt_hh = genotypecolordict(P11_TI_NEC_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P11_TI_NEC_hh_data_heatmap,P11_TI_NEC_color_dict_gt_hh,P11_TI_NEC_row_colors_gt_hh,title)
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In [ ]:
# Load data
P14_COLON_C_gt_h_h = pd.read_csv('Different_Time_Points/P14_COLON_WT_Homo_HET/CONTROL/tsv/data_normalized.txt',sep='\t',index_col=0)
P14_COLON_C_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P14_COLON_WT_Homo_HET/CONTROL/tsv/P14_COLON_WT_Homo_HET_C.txt',sep='\t',index_col=0)
P14_COLON_C_gt_metadata_h_h = format_meta(P14_COLON_C_gt_metadata_h_h,"Genotype")
P14_COLON_C_h_h_data_heatmap = create_heatmap(P14_COLON_C_gt_h_h,P14_COLON_C_gt_metadata_h_h,"Genotype")

title = "P14 COLON Genotype WT vs Homo vs Het Control"
P14_COLON_C_color_dict_gt_h_h,P14_COLON_C_row_colors_gt_h_h = genotypecolordict(P14_COLON_C_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P14_COLON_C_h_h_data_heatmap,P14_COLON_C_color_dict_gt_h_h,P14_COLON_C_row_colors_gt_h_h,title)
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In [ ]:
# Load data
P14_COLON_C_gt_hh = pd.read_csv('Different_Time_Points/P14_COLON_WT_HomoHET/tsv/data_normalized.txt',sep='\t',index_col=0)
P14_COLON_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P14_COLON_WT_HomoHET/tsv/P14_COLON_WT_HomoHET.txt',sep='\t',index_col=0)
P14_COLON_C_gt_metadata_hh = format_meta(P14_COLON_C_gt_metadata_hh,"Genotype")
P14_COLON_C_hh_data_heatmap = create_heatmap(P14_COLON_C_gt_hh,P14_COLON_C_gt_metadata_hh,"Genotype")

title = "P14 COLON Genotype WT vs Homo_Het Control"
P14_COLON_C_color_dict_gt_hh,P14_COLON_C_row_colors_gt_hh = genotypecolordict(P14_COLON_C_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P14_COLON_C_hh_data_heatmap,P14_COLON_C_color_dict_gt_hh,P14_COLON_C_row_colors_gt_hh,title)
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Take a look¶

In [ ]:
# Load data
P14_TI_C_gt_h_h = pd.read_csv('Different_Time_Points/P14_Ileal_WT_Homo_HET/tsv/data_normalized.txt',sep='\t',index_col=0)
P14_TI_C_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P14_Ileal_WT_Homo_HET/tsv/P14_Ileal_WT_Homo_HET_C.txt',sep='\t',index_col=0)
P14_TI_C_gt_metadata_h_h = format_meta(P14_TI_C_gt_metadata_h_h,"Genotype")
P14_TI_C_h_h_data_heatmap = create_heatmap(P14_TI_C_gt_h_h,P14_TI_C_gt_metadata_h_h,"Genotype")

title = "P14 TI Genotype WT vs Homo vs Het Control"
P14_TI_C_color_dict_gt_h_h,P14_TI_C_row_colors_gt_h_h = genotypecolordict(P14_TI_C_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P14_TI_C_h_h_data_heatmap,P14_TI_C_color_dict_gt_h_h,P14_TI_C_row_colors_gt_h_h,title)
No description has been provided for this image
In [ ]:
# Load data
P14_TI_C_gt_hh = pd.read_csv('Different_Time_Points/P14_Ileal_WT_HomoHET/tsv/data_normalized.txt',sep='\t',index_col=0)
P14_TI_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P14_Ileal_WT_HomoHET/tsv/P14_Ileal_WT_HomoHET.txt',sep='\t',index_col=0)
P14_TI_C_gt_metadata_hh = format_meta(P14_TI_C_gt_metadata_hh,"Genotype")
P14_TI_C_hh_data_heatmap = create_heatmap(P14_TI_C_gt_hh,P14_TI_C_gt_metadata_hh,"Genotype")

title = "P14 TI Genotype WT vs Homo_Het Control"
P14_TI_C_color_dict_gt_hh,P14_TI_C_row_colors_gt_hh = genotypecolordict(P14_TI_C_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P14_TI_C_hh_data_heatmap,P14_TI_C_color_dict_gt_hh,P14_TI_C_row_colors_gt_hh,title)
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In [ ]:
# Load data
P28_COLON_C_gt_h_h = pd.read_csv('Different_Time_Points/P28_COLON_WT_Homo_HET/tsv/data_normalized.txt',sep='\t',index_col=0)
P28_COLON_C_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P28_COLON_WT_Homo_HET/tsv/P28_COLON_WT_Homo_HET.txt',sep='\t',index_col=0)
P28_COLON_C_gt_metadata_h_h = format_meta(P28_COLON_C_gt_metadata_h_h,"Genotype")
P28_COLON_C_h_h_data_heatmap = create_heatmap(P28_COLON_C_gt_h_h,P28_COLON_C_gt_metadata_h_h,"Genotype")

title = "P28 COLON Genotype WT vs Homo vs Het Control"
P28_COLON_C_color_dict_gt_h_h,P28_COLON_C_row_colors_gt_h_h = genotypecolordict(P28_COLON_C_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P28_COLON_C_h_h_data_heatmap,P28_COLON_C_color_dict_gt_h_h,P28_COLON_C_row_colors_gt_h_h,title)
No description has been provided for this image
In [ ]:
# Load data
P28_COLON_C_gt_hh = pd.read_csv('Different_Time_Points/P28_COLON_WT_HomoHET/tsv/data_normalized.txt',sep='\t',index_col=0)
P28_COLON_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P28_COLON_WT_HomoHET/tsv/P28_COLON_WT_HomoHET.txt',sep='\t',index_col=0)
P28_COLON_C_gt_metadata_hh = format_meta(P28_COLON_C_gt_metadata_hh,"Genotype")
P28_COLON_C_hh_data_heatmap = create_heatmap(P28_COLON_C_gt_hh,P28_COLON_C_gt_metadata_hh,"Genotype")

title = "P28 COLON Genotype WT vs Homo_Het Control"
P28_COLON_C_color_dict_gt_hh,P28_COLON_C_row_colors_gt_hh = genotypecolordict(P28_COLON_C_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P28_COLON_C_hh_data_heatmap,P28_COLON_C_color_dict_gt_hh,P28_COLON_C_row_colors_gt_hh,title)
No description has been provided for this image
In [ ]:
# Load data
P28_TI_C_gt_h_h = pd.read_csv('Different_Time_Points/P28_Ileal_WT_Homo_HET/tsv/data_normalized.txt',sep='\t',index_col=0)
P28_TI_C_gt_metadata_h_h = pd.read_csv('Different_Time_Points/P28_Ileal_WT_Homo_HET/tsv/P28_Ileal_WT_Homo_HET.txt',sep='\t',index_col=0)
P28_TI_C_gt_metadata_h_h = format_meta(P28_TI_C_gt_metadata_h_h,"Genotype")
P28_TI_C_h_h_data_heatmap = create_heatmap(P28_TI_C_gt_h_h,P28_TI_C_gt_metadata_h_h,"Genotype")

title = "P28 TI Genotype WT vs Homo vs Het Control"
P28_TI_C_color_dict_gt_h_h,P28_TI_C_row_colors_gt_h_h = genotypecolordict(P28_TI_C_gt_metadata_h_h,"Genotype")
plot_cluster_heatmap(P28_TI_C_h_h_data_heatmap,P28_TI_C_color_dict_gt_h_h,P28_TI_C_row_colors_gt_h_h,title)
No description has been provided for this image
In [ ]:
# Load data
P28_TI_C_gt_hh = pd.read_csv('Different_Time_Points/P28_Ileal_WT_HomoHET/tsv/data_normalized.txt',sep='\t',index_col=0)
P28_TI_C_gt_metadata_hh = pd.read_csv('Different_Time_Points/P28_Ileal_WT_HomoHET/tsv/P11_P14_P28_Ileal_WT_HomoHET.txt',sep='\t',index_col=0)
P28_TI_C_gt_metadata_hh = format_meta(P28_TI_C_gt_metadata_hh,"Genotype")
P28_TI_C_hh_data_heatmap = create_heatmap(P28_TI_C_gt_hh,P28_TI_C_gt_metadata_hh,"Genotype")

title = "P28 TI Genotype WT vs Homo_Het Control"
P28_TI_C_color_dict_gt_hh,P28_TI_C_row_colors_gt_hh = genotypecolordict(P28_TI_C_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P28_TI_C_hh_data_heatmap,P28_TI_C_color_dict_gt_hh,P28_TI_C_row_colors_gt_hh,title)
No description has been provided for this image
In [ ]:
# Load data
P11_c_gt_hh = pd.read_csv('Different_Time_Points/P11_COLON_WT_HomoHET/CONTROL/TSV/data_normalized.txt',sep='\t',index_col=0)
P11_c_gt_metadata_hh = pd.read_csv('Different_Time_Points/P11_COLON_WT_HomoHET/CONTROL/TSV/P11_COLON_WT_HomoHET_C.txt',sep='\t',index_col=0)
P11_c_gt_metadata_hh = format_meta(P11_c_gt_metadata_hh,"Genotype")
P11_C_hh_data_heatmap = create_heatmap(P11_c_gt_hh,P11_c_gt_metadata_hh,"Genotype")

title = "P11 COLON Genotype WT vs Homo_Het CONTROL"
P11_C_color_dict_gt_hh,P11_C_row_colors_gt_hh = genotypecolordict(P11_c_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P11_C_hh_data_heatmap,P11_C_color_dict_gt_hh,P11_C_row_colors_gt_hh,title)
No description has been provided for this image
In [ ]:
# Load data
P11_NEC_gt_hh = pd.read_csv('Different_Time_Points/P11_COLON_WT_HomoHET/NEC/TSV/data_normalized.txt',sep='\t',index_col=0)
P11_NEC_gt_metadata_hh = pd.read_csv('Different_Time_Points/P11_COLON_WT_HomoHET/NEC/TSV/P11_COLON_WT_HomoHET_NEC.txt',sep='\t',index_col=0)
P11_NEC_gt_metadata_hh = format_meta(P11_NEC_gt_metadata_hh,"Genotype")
P11_NEC_hh_data_heatmap = create_heatmap(P11_NEC_gt_hh,P11_NEC_gt_metadata_hh,"Genotype")

title = "P11 COLON Genotype WT vs Homo_Het NEC"
P11_NEC_color_dict_gt_hh,P11_NEC_row_colors_gt_hh = genotypecolordict(P11_NEC_gt_metadata_hh,"Genotype")
plot_cluster_heatmap(P11_NEC_hh_data_heatmap,P11_NEC_color_dict_gt_hh,P11_NEC_row_colors_gt_hh,title)
No description has been provided for this image